Mathematics For Machine Learning Course
What will I learn?
Enhance your Business Intelligence skills with our Mathematics for Machine Learning Course. Delve into data exploration, becoming proficient in methods to identify outliers and manage missing values. Learn data preprocessing, including normalisation and outlier treatment, to improve model accuracy. Explore machine learning algorithms for time series data, such as decision trees and ARIMA. Gain expertise in feature engineering, optimisation techniques, and model evaluation. This course provides you with practical, high-quality skills for real-world applications.
Apoia's Unique Features
Develop skills
Strengthen the development of the practical skills listed below
Master data structures: Analyse and interpret complex data sets effectively.
Detect outliers: Identify anomalies to enhance data accuracy and reliability.
Apply time series models: Use ARIMA and LSTM for precise forecasting.
Optimise algorithms: Implement gradient descent for efficient model training.
Engineer features: Create polynomial features to improve model performance.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can change the chapters and workload.
- Choose which chapter to start with
- Add or remove chapters
- Increase or decrease the course workload
Examples of chapters you can add
You will be able to generate more chapters like the examples below
This is a free course focused on personal and professional development. It is not equivalent to a technical, undergraduate, or postgraduate course, but it offers practical and relevant knowledge for your professional journey.